5 papers · 1 filter
Toward Physically Consistent Driving Video World Models under Challenging Trajectories
Jiawei Zhou, Zhenxin Zhu, Lingyi Du +10
Video generation models have shown strong potential as world models for autonomous driving simulation. However, existing approaches are primarily trained on real-world driving data…
Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
Jiawei Zhou, Linye Lyu, Daojing He +1
Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differ…
SafeMVDrive: Multi-view Safety-Critical Driving Video Synthesis in the Real World Domain
Jiawei Zhou, Linye Lyu, Zhuotao Tian +2
Safety-critical scenarios are rare yet pivotal for evaluating and enhancing the robustness of autonomous driving systems. While existing methods generate safety-critical driving tr…
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation
Jiawei Zhou, Linye Lyu, Daojing He +1
Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differ…
CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors
Linye Lyu, Jiawei Zhou, Daojing He +1
Prior works on physical adversarial camouflage against vehicle detectors mainly focus on the effectiveness and robustness of the attack. The current most successful methods optimiz…